Episodic Memory in Agentic Frameworks: Suggesting Next Tasks
Sandro Rama Fiorini, Leonardo G. Azevedo, Raphael M. Thiago, Valesca M. de Sousa, Anton B. Labate, Viviane Torres da Silva

TL;DR
This paper introduces an episodic memory system for agentic frameworks with LLMs, enabling better next-task suggestions by leveraging past workflows, reducing hallucination, and avoiding extensive fine-tuning.
Contribution
It proposes a novel episodic memory architecture that enhances task recommendation in agentic workflows without relying solely on LLMs or extensive fine-tuning.
Findings
Improved next-task suggestion accuracy.
Reduced reliance on hallucination-prone LLM outputs.
Effective retrieval of relevant past workflows.
Abstract
Agentic frameworks powered by Large Language Models (LLMs) can be useful tools in scientific workflows by enabling human-AI co-creation. A key challenge is recommending the next steps during workflow creation without relying solely on LLMs, which risk hallucination and require fine-tuning with scarce proprietary data. We propose an episodic memory architecture that stores and retrieves past workflows to guide agents in suggesting plausible next tasks. By matching current workflows with historical sequences, agents can recommend steps based on prior patterns.
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Taxonomy
TopicsScientific Computing and Data Management · Machine Learning in Materials Science · Business Process Modeling and Analysis
